I’m reading a book on consciousness and language by Gerald Edelman and he’s discussing the requirements for formation of consciousness in this section.
I know evolutionary psychology is highly controversial, but this theory seems more biological and reasonable to me.
Thoughts?

What properties would information need to have in order to become effectively incomprehensible to the human brain, even if it were fully observable and accessible?
I’m thinking of information that we can perceive or measure, but whose underlying structure is too difficult for a human mind to meaningfully process or understand.
Would the main limitation be information capacity—simply too much data for the brain to process—or structural complexity, where even a relatively limited amount of information has relationships and dependencies that are too complex to mentally represent?
Or is true cognitive “indigestibility” more likely to require both: an enormous amount of information combined with a highly complex structure?
More generally, could there be information that is physically observable but fundamentally beyond human cognitive architecture to comprehend?

In an early part of my life I worked as a post-grad Psychology researcher within a university. Recently I have been seeing a lot regarding "Aphantasia" and I had a weird sense of some concern regarding how it was being discussed and described.

So I have been digging into the cognitive science and neuroimaging data surrounding aphantasia and I believe I have identified what causes me concern and would like to share this with others and see what the response is.

While the condition is a measurable neurological reality, there is a critical selection bias in how researchers are currently studying it.

Basically, the current scientific consensus is likely missing a massive demographic, resulting in a skewed understanding of the baseline condition. Here is why I believe this to be the case:

1. We are testing the wrong way around (Flawed Screening)

Right now, the foundational physiological studies confirming aphantasia rely on a subjective survey called the Vividness of Visual Imagery Questionnaire (VVIQ) to find participants. Researchers take people who score a zero on the VVIQ, then run more objective physiological tests on them, such as measuring pupillary light response or binocular rivalry tests. Unsurprisingly, the people who self report having no visual imagery also fail the physiological tests.

However, to establish true population prevalence and eliminate semantic misunderstandings, this testing architecture must be inverted. Researchers need to conduct randomized, objective physiological screening (like pupil dilation) on the general population first, isolate those who lack the physiological response, and then see how they answer the VVIQ. Until we invert the pipeline, we are only studying people who possess the high degree of metacognition required to realize their internal rendering differs from the norm.

Sources:

• Pupillary light response validation: https://elifesciences.org/articles/72484

• Binocular rivalry validation: https://pubmed.ncbi.nlm.nih.gov/29175093/

2. The STEM and IQ/Intelligence Overrepresentation

While this is always a problem it is a particular issue here because of the mechanics of how aphantasia is tested. Because recruitment relies heavily on self reporting and university populations, current aphantasia cohorts are packed with individuals in Science, Technology, Engineering, and Mathematics (STEM). This means the literature is observing aphantasia exclusively through the lens of individuals with high baseline fluid intelligence and high working memory capacity. While this is a fairly generic problem, not specific to aphantasia, it massively compounds with the next issue.

Source:

• STEM demographic distribution: https://www.sciencefocus.com/news/people-with-aphantasia-are-more-likely-to-work-in-a-stem-field

3. High IQ masks the deficit via algorithmic workarounds

In the studies conducted thus far, when aphantasic participants cannot rely on the computationally "cheap" heuristic of a mental picture, their brain has to work much harder to solve a spatial puzzle. It has to route the problem through propositional logic and spatial motor networks.

When researchers test aphantasics on spatial reasoning (like 3D mental rotation tasks), they find they are just as accurate as typical imagers, but they have significantly slower reaction times. A highly intelligent person has the raw working memory bandwidth to brute force this algorithmic heavy lifting. They get the right answer, it just takes them a fraction of a second longer. But this leaves less intelligent people simply unable to complete the task.

Source:

• Mental rotation accuracy and reaction times: https://pubmed.ncbi.nlm.nih.gov/35180481/

4. The missing cohort: True aphantasia without the cognitive buffer

This is the serious gap in the current literature. If someone has average or lower fluid intelligence AND they cannot render visual heuristics, they lack the working memory capacity to execute that expensive logical workaround.

Instead of showing high accuracy and high latency on spatial tasks, their working memory will simply saturate, resulting in task failure. Because they fail, they are highly likely to be clinically categorized as having a general learning issues, poor memory, or a specific visuospatial deficit. AND, because they lack the metacognitive buffer to isolate their lack of mental imagery as the root cause, they are also less likely to self-identify as aphantasic. Currently the demographics cited above indicate over 20 percent of aphantasics work in these highly technical/STEM fields. Is this because they are genuinely over-represented or because we struggle to identify aphantasics outside of "intelligent" cohorts due to the above.

The Bottom Line

Current cognitive science claims that aphantasia does not inherently limit spatial reasoning. But that is only because researchers are almost certainly exclusively studying highly compensated individuals who possess the raw computational power to bypass the visual deficit. And this focus is NOT driven purely by traditional WEIRD selection bias. Until we establish objective general population screening, we are not studying baseline aphantasia; we are only studying high IQ compensation mechanics.

You both require high intelligence and capable meta-cognition to both identify and be identified as having aphantasia. This likely leads to over-diagnosis of intelligent participants who can readily exploit non-visual cognition for task completion, and massive under-diagnosis of those who have no alternative cognitive approaches and lack sufficient meta-cognition to articulate their issues.

Thank you for coming to my TED talk.

So i have a bachelor's degree in linguistics. I'm planning on doing masters in cognitive science. I know python, i can speak 3 languages + sign language. I've written a paper on computational linguistics and a paper on psycho/neurolinguistics. I was wondering if i'll be left jobless with all these or is there an opportunity to find a well paying job? I'm interested in NLP but also in SLP. I'd appreciate if someone could share their experience w me. Thank you in advance.

Am I the only one getting sick of talk about people “processing” something that has happened to them? What happened to ordinary words like “think about” and “reflect on”? “Processing” is what computers do. Not human minds.

Suppose a person genuinely understands something internally, but cannot fully express that understanding.

I don’t mean merely being unable to find the right words. Assume we are allowed to use any means of expression available to us: spoken or written language, mathematics, diagrams, paintings, music, animations, simulations, physical demonstrations, and so on.

Could there still be forms of understanding that cannot be adequately externalized through any of these methods?

I’d like to pose an experimental question rather than present a new theory of mind.

Suppose we have two longitudinal human–LLM interactions where we control for:

• model and version;
• system prompt;
• information available about the participant;
• memory and retrieval;
• degree of personalization;
• accessible context.

The main difference is that one dyad retains its own interaction history, while the other receives an equivalent amount of relevant information drawn from a different or yoked history.

Should we expect the two conditions to become functionally equivalent?

Or can the temporal organization of a specific interaction history retain predictive information that is not captured simply by making the same content available?

This is the question I have been trying to formalize through a framework I called the Shared Cognitive Field (CCC / Campo Cognitivo Condiviso).

By “field” I do not mean a physical field, a shared consciousness, or a new entity.

I use the term as shorthand for a possible structure of observable dependencies at the level of interaction.

The starting point is not especially exotic. It draws on existing work in extended cognition, distributed cognition, interactive alignment, and coordination in dialogue.

Recent work on LLM interaction makes the competing explanations particularly important. Humans adapt their communication strategies when interacting with LLMs; LLMs can adapt linguistically to conversational partners; and external memory/retrieval systems can already produce substantial continuity and personalization.

I therefore treat these mechanisms as competing baselines, not as evidence for the framework.

The modest hypothesis is:

In 2025 I proposed a provisional composite measure, CQI(t), involving shared information, predictive coherence, interactional coordination, stability after perturbation, and affective-relational alignment.

I do not regard CQI as a validated metric. Its proxies, weights, and factor structure would all need empirical testing.

A simple design I am considering would compare four conditions after a longitudinal interaction phase:

A — Original dyad
Same participant, same interaction history.

B — Yoked-history control
Same amount of relevant information, but derived from another dyad’s history.

C — Memory-ablated control
Same dyad, but without access to longitudinal memory.

D — Partner-swap control
Comparable informational state, different human partner.

A controlled perturbation could then be introduced.

Possible outcome measures might include:

• prediction of the partner’s next-turn intention or decision;
• number of turns required to repair misunderstandings;
• collaborative task performance;
• ability to recover the correct prior rationale behind a decision;
• recovery of coordination after perturbation.

The discriminating prediction would be straightforward:

If interaction history contributes nothing beyond available information, the original dyad should show no systematic advantage over a well-matched yoked control.

If that is what the data show, the relational construct should be reduced or discarded.

If a replicable residual effect remains, I would not interpret it as “shared consciousness” or strong emergence.

The next question would simply be:

What interaction-level variable have we failed to model?

One transparency note: the original Zenodo paper from November 2025 contains some stronger exploratory language, including a numerical threshold associated with a phenomenological concept I called Noosemia.

I no longer regard that threshold as empirically established.

In the current framing, both CQI and Noosemia are provisional hypotheses requiring validation.

So my question to r/cogsci is fairly concrete:

Is there already an established construct in cognitive science, HCI, or dialogue research that makes this proposed “interaction-history effect” unnecessary?

And if not:

What is the simplest experiment that could falsify it?

Original paper, for provenance:

https://doi.org/10.5281/zenodo.17672256

Disclosure: AI tools have been used during drafting, translation, and critical review of this project. I take responsibility for the claims, references, and proposed methodology.

Hi all,

I recently decided to major in Cognitive Science instead of Computer Science as I couldn't meet the requirements.

looking at all the career posts on here is making me worried about career viability in this field and I wanted to ask, do any previous grads or current grads have any experience with the job marker as a Cogsci major? Do I have anything to worry about in terms of career viability? just to know if I should switch major now or keep pursuing it.

if it helps, I have knowledge in programming and im very interested in ML and AI. I'm also quite keen on learning more about psychology.

Like for me it's quotes they feel very good when heated but when u understand them emotionally they give real meaning

I recently realized that i count in my head when i do stuffs. Like, i fill a water bottle right? I will count 1,2,3 in my head untill its full. Or I'll be chopping some vegetables and I'll count how many times i cut it. Or when i walk from point A to point B, i will count my steps. It's not like i remember how many steps it takes me or anything. I just always count. Do you guys do that too? Is it just me?

One of the most remarkable things about the human mind is its ability to generalise.

We don’t just learn specific instances ,we extract patterns, form categories, and apply knowledge to situations we’ve never encountered before. From a child saying “goed” to an expert transferring insight across domains, generalisation sits at the heart of flexible intelligence.

Cognitive science has studied this from multiple angles:

Stimulus generalisation gradients

Prototype vs. exemplar models of categories

Analogical reasoning and transfer of learning

The fine line between useful abstraction and costly overgeneralisation

What fascinates me is how the mind decides how far to generalise. Too little, and we fail to transfer valuable knowledge. Too much, and we apply rules where they don’t belong.

In a world increasingly shaped by both human and artificial intelligence, understanding biological generalisation feels more relevant than ever.

What’s a recent insight (or classic finding) about generalisation that changed how you think about learning or decision-making?

For example, if you occasionally put your keys in a different location and have to notice and remember where they are instead of relying on the automatic habit of picking them up from the same spot.

I have been learning german for quite a wihle now (about 2 years) and i have reached b1 and now learning b2 but I'm facing a problem that sometimes I forget what I'm speaking about not the words in german I mean the topic itself and the words are incredibly hard to remember not because they are hard to pronounce or long but because I js don't remember them and overall i have a decent memory and a bad attention span. I came across dual nback a couple days ago and didn't even solve n2 with a good accuracy and I saw some people debating whether it's effective or not so I admit I asked ai but I don't really trust claud so does it work or not because I don't want to wast my time, PLEASE DON'T REPLY WITH AI I BEG YOU I HAVE ALREADY ASKED AI.

If you have some tips for my situation put them in the comments

It seems AI has allowed to everyone to be a researcher these days! What I've seen on this sub is absolute drivel, almost every day, of people posting their own inane "theories."

i can go weeks, sometimes months, without doing something and genuinely think i’m over it

then one stressful week happens and suddenly i’m doing the exact same thing again. not even as a conscious choice, it just sort of happens

which makes me wonder if the habit was ever actually gone, or if it was still sitting somewhere in the brain and stress just made it easier to fall back into

it’s weird how quickly we return to familiar behavior when we’re tired, anxious or overwhelmed, even when we know it won’t help

has anyone here read anything interesting about this? or noticed the same thing in themselves?

Hi all,

I'm an international student with a background spanning linguistics, philosophy, and computational approaches to cognition. My main interests sit at the intersection of formal linguistics, philosophy of mind, logic, and computation.

My GPA is around 3.5, and while I know research experience and fit matter more than grades in this field, I'd love some outside perspective on:

• Which programs are known for strong interdisciplinary work between linguistics, logic/computation, and cognitive science?
• Any programs where faculty are especially open to applicants coming from a more theoretical/linguistics-philosophy background rather than a pure cog-neuro or psych background?
• How much weight international applicants should expect GPA to carry compared to research fit and letters?

I have some research experience but not a ton yet, and I'm working on building more before applying. Just trying to get a sense of where to focus my energy before I start reaching out to potential advisors.

Thanks in advance for any input.

Hey all, currently wondering what I would be able to get out of a cognitive science major and if it serves well down career paths. I am thinking about applying cogsci just because I have so many interests and want a major that keeps many fields/opportunities open. (ui/ux, sensory science, behavioral economics— maybe its own major, design, ui/ux research or market research/consulting, computing/CS, AI/human interaction…and probably more)

I am concerned because I see online it is a major that is mostly on track to academia or won’t help much with getting a job. As someone who would prefer to work in industry after BA and not grad school (open to opinions on that as well), it’s a little worrying.

Any current/graduated cogsci majors, I would love to hear your insight!

1. I was so good at coordinate geometry I could look at an equation and say what the curve is. What its center is, etc. I could weirdly visualise it in wireframes. I used to teach the lecturer sometimes
2. I liked optimising space for component packaging. For example, we have to package drone parts for easy assembly and make it as compact as possible
3. I was previously a computational design engineer, and CAD was fun, but I felt out of love for it because I don't know why. I have AuDHD; maybe the burnout, idk
4. I liked optimizing enclosure designs for compactness and cost. Making it as simple and functional as possible
5. I like designing clean, simple things
6. I like graphs and networks, but I'm sure I'm good at this in any useful way. have very poor working memory for doing complex math. Although I love doing math
7. I like visualisation tools like 3d CAD tools, Miro, kinda flowchart tools, etc

I am really trying to make sense of a suitable career after having burnout from my previous product manager role. I have only been working part-time ever since and am trying to treat my generalised anxiety disorder as well

Please guide this soul if you have figured out a suitable career with similar traits or even the approach to figure it career from what suits your brain, from an energy point rather than an interest point

The preprint can be accessed via the following link: https://arxiv.org/abs/2608.12408 (q-bio.NC / cs.LG). And for the code: https://github.com/nilsleut/evaluation-resolution-rsa

The following assertion is frequently made in model-brain comparisons: untrained convolutional neural networks (CNNs) have the capacity to match or surpass backpropagation-trained CNNs at the early visual cortex (V1) in recurrent self-attention (RSA). The present study demonstrates that this phenomenon is predominantly an artefact of evaluation resolution.

The configuration comprised a small CNN trained at 32px (CIFAR-10 subset), five learning rules (random init, backprop, feedback alignment, predictive coding, STDP), and was evaluated on THINGS-fMRI stimuli at six resolutions from 32px to 224px. The weights and normalisation were held fixed.

The primary outcome of this study is the observation of a discrepancy between the trained and untrained backpropagation (BP) V1 gap, which exhibits a non-monotonic trend across the range of image sizes examined. Specifically, the gap narrows from −0.001±0.007 at 32 pixels to +0.044±0.006 at 224 pixels, a pattern that persists consistently across the entire image size range (n=5 seeds). The model incorporates five rule conditions, human fMRI, directionally single-seed macaque ephys, the full training trajectory, and two off-the-shelf 224px-trained models (ResNet-50, Swin-Tiny). Therefore, the presence of an artifact resulting from a mismatch between training and evaluation is not a contributing factor, since these models also peak at low resolution.

Following the implementation of bit-identical-weight interventions wherever possible, the following were ruled out: train/eval resolution matching, Gabor/pixel low-level structure, untrained baseline's uncalibrated batch-norm, and convergence of pooled features towards global brightness (though a single scalar luminance value did reach ρ=0.075 against V1, essentially matching the untrained network's own 0.076 — this is a separate, disconcerting result regarding the limitations of this comparison style).

A content-vs-pooling control (cap image detail at 32px, upsample, vs. allow content variation) demonstrates that the dependence is predominantly contingent on image content, rather than the number of pooled positions.

The investigation revealed that one effect does survive across all resolutions. In particular, the backprop > untrained at LOC effect was observed at every resolution that was tested. It is evident that the process of learning can result in a noticeable impact, albeit not in the conventional areas typically associated with V1 comparisons.

In addition, the following issues were identified:

This process revealed a batch-norm evaluation mode bug in three of the earlier preprints, which have now been corrected in this release (correction notes on the Arxiv pages).

I'm happy to get feedback, especially on the way we've framed the discussion on receptive-field matching (as in Laskar et al. 2018). I think it's suggestive, but I didn't test it directly.

Hello all,

I've recently conducted an independent analysis of the RIOT IQ test based on publicly accessible data, as well as their bulletin.

You can read the report here: https://doi.org/10.5281/zenodo.21972419

I'd appreciate any feedback I can get!

Thanks